Taming Complexity in Materials Modeling
Taming Complexity in Materials Modeling
Disciplines
Chemistry (30%); Physics, Astronomy (70%)
Keywords
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Surface Science,
Oxide Surfaces And Interfaces,
Catalysis,
Computational Materials Science,
First-Principles Calculations,
Machine Learning
How materials behave at the smallest scale how atoms re-arrange themselves at surfaces and how they react to their environment and external stimuli is reasonably well understood: with quantum mechanical methods one can model relatively simple processes and compare them with well-controlled experiments. When systems become more complex, however, when a material contains many different elements or when it is exposed to gas atmospheres or a liquid, then these methods quickly reach their limits. In this Coordinated Research Project, SFB TACO, experimental and theoretical groups from physics and chemistry at TU Wien and Universität Wien work together closely to propel such methods a big step forward. The goal is to drastically accelerate such cal culations using Machine Learning methods. The project focuses on oxides, i.e., compounds of metals with oxygen. These materials are amongst the most common inorganic substances on our planet. Depending on their composition, they change their chemical and physical properties. This is both a blessing and a curse: On the one hand, this wide range makes it possible to achieve materials properties that are tailor-made for technological applications. On the other hand, the large variety of their structures, especially on their surfaces, makes it particularly hard to model these materials. Oxides are used in energy storage, in the conversion of solar energy into chemical energy, and in catalysis. The underlying processes and phenomena need to be well understood in order to realize better energy and materials conversion schemes, and the SFB TACO goals make a significant contribution towards methods development and scientific insights. The project team has at their disposal an array of experimental methods, which they will use to study a material under a wide range of conditions from single crystals in vacuum to technical powder samples under reaction conditions. So-called hand-shake methods, commonly used by all experimental groups ensure transferability of results. The theoretical working groups apply various machine learning approaches, from computer vision to the modeling of surface reactions and spectra. These methods are put to test and refined in close collaboration with the experimental groups.
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consortium member (1.3.2021 - 28.2.2025)
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consortium member (1.3.2021 - 28.2.2025)
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consortium member (1.3.2021 - 28.2.2025)
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consortium member (1.3.2021 - 28.2.2025)
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consortium member (1.3.2021 - 28.2.2025)
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consortium member (1.3.2021 - 28.2.2025)
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consortium member (1.3.2021 - 28.2.2025)
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consortium member (1.3.2021 - 28.2.2025)
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consortium member (1.3.2021 - 28.2.2025)
- Technische Universität Wien
Research Output
- 5 Citations
- 4 Publications
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2024
Title Engineering Materials for Catalysis DOI 10.3390/catal14050293 Type Journal Article Author Pintar A Journal Catalysts Pages 293 Link Publication -
2024
Title Infrared reflection absorption spectroscopy setup with incidence angle selection for surfaces of non-metals DOI 10.1063/5.0210860 Type Journal Article Author Rath D Journal Review of Scientific Instruments Pages 065106 Link Publication -
2024
Title Highly Stable Self-Cleaning Paints Based on Waste-Valorized PNC-Doped TiO2 Nanoparticles DOI 10.1021/acscatal.3c06203 Type Journal Article Author Maqbool Q Journal ACS Catalysis Pages 4820-4834 Link Publication -
2024
Title Spatially resolved uncertainties for machine learning potentials DOI 10.26434/chemrxiv-2024-k27ps Type Preprint Author Heid E Link Publication